1. Technical Field
The present invention relates in general to an improved method and system for real time pricing of fine-grained resources and in particular to an improved method and system for real time pricing of fine-grained resources utilizing a distributed computing network. Still more particularly, the present invention relates to an improved method and system for real time pricing of fine-grained resources which permits efficient and intelligent multi-parameter purchasing/consumption of fine-grained resources.
2. Description of the Related Art
Producers of goods and services typically set their prices in accordance with the cost of production and the expected demand for those goods or services. However, for a large class of producers, the cost of a particular item or class of service is dependent upon the demand for that item or class of service. For example, electrical utilities typically have a fixed maximum generating capacity. It is quite expensive for an electrical power generation station to be switched on and off and this consideration causes the incremental cost of electrical power to be quite low when the demand for electrical power is low. As the demand for electrical power increases, the incremental cost of energy rises. When demand is close to the maximum power generation capacity, the cost of additional electrical energy becomes quite high, resulting in a demand for the construction of increased generation capacity and a requirement for electrical power generation stations to preemptively disconnect consumers from the electrical power distribution network.
Traditionally, this problem has been addressed by relying upon a pricing scheme which takes anticipated demand into account. For example, during the summer when high air conditioning costs are expected electrical power costs are set higher than during the winter. Similarly, gasoline prices rise during the summer months in anticipation of increased consumption by drivers. However, since both of these solution rely upon the prediction of future demand, they can lead to significantly flawed pricing models.
One solution to this problem is a real time pricing model in which the price is dynamically adjusted periodically to match demand for the goods or services. One example of this model is short term trading on the stock exchange. Barring news that substantially changes the inherent value of the stock of a particular company, the price of that stock is directly related to the demand for that stock. Another example of this pricing model is telecommunications network capacity auctions which are provided in real time by companies like Arbinet, at xe2x80x9cwww.arbinet.com.xe2x80x9d
Real time pricing models currently exist in situations where a large quantity of a resource exchanges hands. In both stock markets and telecom network capacity auctions, the buyers and sellers meet at an exchange to trade large quantities of stocks and telecommunication capacity. The inherent efficiencies of real time pricing models make them extremely attractive. However, the overhead of real time price management introduces considerable difficult in extending this model to situations where small quantities of resources are bought and sold.
Glorioso, et al., U.S. Pat. No. 5,926,776 discloses one system in which the consumption of electrical power is controlled by price points. A smart thermostat is utilized having a transceiver for two-way communication with the energy provider. The smart thermostat includes a temperature sensor for measuring a temperature, a user interface for displaying and receiving information to and from the user, a port connecting the thermostat to a cooling or heating device, a processor and a transceiver. The transceiver receives a current energy price from an energy provider and the user interface receives temperature set points and associated acceptable energy costs. The processor then issues a control signal to the cooling or heating device to operate when the temperature is different than the temperature set point and the acceptable energy cost is not greater than the current price of energy. Information regarding the temperature set points and associated costs are transmitted by the transceiver from the smart thermostat to the energy provider which may then predict the effect that a change in the current energy price will have on energy demand. The user interface may also display a bill for the accumulated use of energy either calculated by the processor or downloaded from the energy provider for utilization by the user.
Similarly, U.S. Pat. No. 5,974,308, issued to Vedel discloses a cellular telephone system which optimizes user demand by charging the system subscribers according to a variable charge rate based upon utilization of each cell within the system by subscribers. The service provider monitors the load in each cell within the cellular system and for each cell a continuously determined charge rate is tailored to specific subscriber categories according to a number of variables which are optimized for an individual cell""s capacity and overall system capacity.
While both of these aforementioned systems describe techniques for varying the real time price of a commodity, both of these techniques are directed to systems which permit the supplier of the commodity to vary the price for that commodity based upon accurate presumptions regarding utilization of that commodity in order to bring additional accuracy to the prediction of future demand and increase the accuracy of pricing models. However, neither of these systems shows or suggests a method or system whereby a user may efficiently and accurately utilize the resultant pricing data to accurately control the purchasing and/or consumption of the goods or services. Consequently, those skilled in the art should appreciate that a need exists for a method and system whereby a user may efficiently and intelligently purchase and consume various fine-grained resources based upon multi-parameter decisions rules.
It is therefore one object of the present invention to provide an improved method and system for real time pricing of fine-grained resources.
It is another object of the present invention to provide an improved method and system for real time pricing of fine-grained resources which utilizes a distributed computing network.
It is yet another object of the present invention to provide an improved method and system for real time pricing of fine-grained resources which permits efficient and intelligent multi-parameter purchasing/consumption decisions to be made.
The foregoing objects are achieved as is now described. A method and system are disclosed for controlling the purchase of fine-grained resource purchases, such as utility resources or access to limited highway lanes. Real time pricing based upon current demand and/or usage is periodically determined. Access to that real time pricing information is obtained by individual users via a distributed computing network or a radio frequency broadcast system and utilization of those resources is then locally controlled based upon that pricing information. Access to limited highway lanes may be priced based upon current actual utilization and pricing information is then broadcast, permitting users to selectively access those lanes based upon real time pricing decisions by those users.
The above as well as additional objects, features, and advantages of the present invention will become apparent in the following detailed written description.